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Skip to Search Results- 10Deep Reinforcement Learning
- 8Control
- 6Reinforcement Learning
- 3Unmanned Aerial Vehicle
- 2Transfer Learning
- 2Visual Servoing
- 1Al-Younes, Younes M. A.
- 1Ebrahimi, Khashayar
- 1Fallahi, Bita
- 1Fidan, Baris
- 1Fink, Geoffrey
- 1Hashemi, Ehsan
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Fall 2019
Q-learning can be difficult to use in continuous action spaces, because a difficult optimization has to be solved to find the maximal action. Some common strategies have been to discretize the action space, solve the maximization with a powerful optimizer at each step, restrict the functional...
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Fall 2023
With the rapid growth of data-intensive applications, congestion control algorithms for datacenter networks under RDMA over Converged Ethernet protocol have become vital in managing various traffic patterns that demand ultra-low latency and high end-to-end throughput. Although many rule-based and...
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Spring 2022
The rapid increase in global water and energy demand due to industrialization and population growth is a pressing challenge humankind faces today. Recent estimates indicate that due to population growth and reduction of water supplies, 40% of the global population is struggling with water...
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Fall 2023
In reinforcement learning (RL), agents learn to maximize a reward signal using nothing but observations from the environment as input to their decision making processes. Whether the agent is simple, consisting of only a policy that maps observations to actions, or complex, containing auxiliary...
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Computer Vision-Based Motion Control and State Estimation for Unmanned Aerial Vehicles (UAVs)
DownloadSpring 2018
To achieve a fully autonomous unmanned aerial vehicle (UAV) the vehicle needs a high level of self awareness. At a minimum it needs to know where it is and where it wants to go. Computer vision (CV) is a logical solution to this problem. However, using CV to solve motion control problems for UAVs...
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Feature Generalization in Deep Reinforcement Learning: An Investigation into Representation Properties
DownloadFall 2022
In this thesis, we investigate the connection between the properties and the generalization performance of representations learned by deep reinforcement learning algorithms. Much of the earlier work on representation learning for reinforcement learning focused on designing fixed-basis...
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Highway Lane change under uncertainty with Deep Reinforcement Learning based motion planner
DownloadSpring 2020
Motion Planning is a fundamental component of a mobile robot to reach its goal safely avoiding collision. For a self-driving car on a highway, the presence of non-communicating vehicles, specially those whose intent is unknown, creates a lot of uncertainty for the motion planner in generating a...
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Spring 2021
There is a growing trend of using unmanned aerial vehicles (also known as UAVs, uncrewed aerial vehicles, or drones) to manipulate and interact with their surroundings. The algorithms and tools used are typically unique to the different tasks performed by UAVs; however, the fundamental UAV system...
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Spring 2022
Reinforcement learning (RL) has shown great success in solving many challenging tasks via the use of deep neural networks. Although the use of deep learning for RL brings immense representational power to the arsenal, it also causes sample inefficiency. This means that the algorithms are...